Impact of wildfires on particulate matter in the Euro-Mediterranean in 2007: sensitivity to some parameterizations of emissions in air quality models

Wildfires and PM in the Euro-Mediterranean in 2007

Impact of wildfires on particulate matter in the Euro-Mediterranean in 2007: sensitivity to some parameterizations of emissions in air quality modelsWildfires and PM in the Euro-Mediterranean in 2007Marwa Majdi et al.

This study examines the uncertainties on air quality modeling associated with
the integration of wildfire emissions in chemistry-transport models (CTMs).
To do so, aerosol concentrations during the summer of 2007, which was marked
by severe fire episodes in the Euro-Mediterranean region especially in the
Balkans (20–31 July, 24–30 August 2007) and Greece (24–30 August 2007),
are analyzed. Through comparisons to observations from surface networks and
satellite remote sensing, we evaluate the abilities of two CTMs,
Polyphemus/Polair3D and CHIMERE, to simulate the impact of fires on the
regional particulate matter (PM) concentrations and optical properties.
During the two main fire events, fire emissions may contribute up to 90 %
of surface PM2.5 concentrations in the fire regions (Balkans and
Greece), with a significant regional impact associated with long-range
transport. Good general performances of the models and a clear improvement of
PM2.5 and aerosol optical depth (AOD) are shown when fires are taken
into account in the models with high correlation coefficients.

Two sources of uncertainties are specifically analyzed in terms of surface PM2.5 concentrations and AOD using
sensitivity simulations: secondary organic aerosol (SOA) formation from
intermediate and semi-volatile organic compounds (I/S-VOCs) and emissions'
injection heights. The analysis highlights that surface PM2.5
concentrations are highly sensitive to injection heights (with a sensitivity
that can be as high as 50 % compared to the sensitivity to I/S-VOC
emissions which is lower than 30 %). However, AOD which is vertically
integrated is less sensitive to the injection heights (mostly below 20 %)
but highly sensitive to I/S-VOC emissions (with sensitivity that can be as
high as 40 %). The maximum statistical dispersion, which quantifies
uncertainties related to fire emission modeling, is up to 75 % for
PM2.5 in the Balkans and Greece, and varies between 36 % and
45 % for AOD above fire regions.

The simulated number of daily exceedance of World Health Organization (WHO)
recommendations for PM2.5 over the considered region reaches 30 days in
regions affected by fires and ∼10 days in fire plumes, which is
slightly underestimated compared to available observations. The maximum
statistical dispersion (σ) on this indicator is also large (with
σ reaching 15 days), showing the need for better understanding of the
transport and evolution of fire plumes in addition to fire emissions.

Vegetation fires are another sizable sporadic source that needs to be
accounted for, especially during summer when the hydrological and
meteorological conditions favor their occurrence and spread. Depending on the
severity of the fire season, their contribution to the atmospheric aerosol
loading and thus to the impairment of the local and regional air quality can
be significant (Barnaba et al., 2011; Rea et al., 2015). However, quantifying their
contribution remains a challenge due to large uncertainty in emissions and
transport.

Most fire episodes in Europe occur in southern countries (Portugal, Spain,
France, Italy, Greece), with ∼500×103ha burned every year
(Barbosa et al., 2009; Turquety et al., 2014). On average, only ∼ 2 % of fires
contribute ∼ 80 % of the area burned due to clusters of fires that
merge into “mega-fires” (San-Miguel Ayanz et al., 2013). Although ignitions are
mainly of anthropogenic origin (negligence, arson, agricultural practices)
according to San-Miguel Ayanz et al. (2013) and the European Forest Fire
Information System (EFFIS) of the European Joint Research Center
(JRC) (JRC, 2008), fire spread depends on meteorological conditions. It
is favored by hot and dry conditions (heat waves and associated droughts),
especially if they are preceded by a wet winter and spring (fuel
accumulation) (Hernandez et al., 2015; Pereira et al., 2005). Fires are also important in
eastern Europe, Ukraine, western Russia and Turkey. They are usually smaller
and associated with agricultural practices like waste burning and land
clearing (Korontzi et al., 2006; Stohl et al., 2007; Turquety et al., 2014).

Turquety et al. (2014) estimate that, on average for the 2003–2010 time period
and the Euro-Mediterranean region, total yearly fire emissions amount to
∼30 % of anthropogenic emissions for PM2.5 (particulate matter
with diameter ≤2.5µm). It is all the more critical as the
fire episodes are concentrated during the summer and usually last less than
10 days (so that emissions are very concentrated in time, resulting in dense
plumes). However, the uncertainty associated with fire emissions is also very
large, estimated to ∼100 % to 200 % (e.g.,
Turquety et al., 2014; Urbanski et al., 2011). Uncertainties are also linked to the
modeling of the temporal variability of emissions. Improving the diurnal
cycle may for instance be critical for some fire events (Rea et al., 2016). In
addition to emissions, the modeling of wildfires' impact on atmospheric
chemistry using chemistry-transport models (CTMs) requires a good knowledge
of the emissions' injection height. Indeed, the energy released by fires can
trigger or enhance convection (pyroconvection), thereby injecting emissions
at high altitude. Several parameterizations have been developed in recent
years (Freitas et al., 2007; Rio et al., 2010; Sofiev et al., 2012) and are increasingly implemented
in CTMs. However, comparisons to observations of fire plume's height
highlight the difficulty in correctly capturing the vertical shape of fire
emissions (Rémy et al., 2017; Sofiev et al., 2012; Val Martin et al., 2012). This will then
influence the simulated transport pathways and their
regional impact. Injecting above the boundary layer will lower the local
impact but result in larger-scale transport. The chemical evolution of fire
plumes is still not well understood and not well represented. Primary organic
aerosols (POA) are directly emitted by biomass burning into the atmosphere.
However, secondary organic aerosols (SOA) are produced through
gas-to-particle partitioning of
oxidation products of volatile organic compounds (VOCs) (with saturation
concentration C* higher than 3.2×106µg m−3),
intermediate organic compounds (I-VOCs) (with saturation concentration
C* in the range of 320–3.2×106µg m−3),
semi-volatile organic compounds (S-VOCs) (with saturation concentration
C* in the range of 0.32–320 µg m−3), and
low-volatility organic compounds (L-VOCs) (with saturation concentration
C* lower than 0.32 µg m−3) (Cappa and Jimenez, 2010; Ciarelli et al., 2017; Fountoukis et al., 2014; Grieshop et al., 2009; Huffman et al., 2009; Lipsky and Robinson, 2006; Murphy et al., 2014; Tsimpidi et al., 2010; Woody et al., 2016). The formation of secondary organic aerosols (SOA) from the
ageing of biomass burning organic precursors is likely to strongly affect
aerosol loading and properties in biomass burning plumes. The major organic
precursors are thought to be intermediate, semi- and low-volatility organic
compounds (I/S/L-VOCs), (Ciarelli et al., 2017; Konovalov et al., 2015; Koo et al., 2014; May et al., 2013a). However,
I/S/L-VOC emissions are not well characterized and their gas-phase emission
is often missing from emission inventories (Robinson et al., 2007). Their
emissions are often estimated from particulate matter emissions
(Ciarelli et al., 2017; Couvidat et al., 2012). The chamber experiments of May et al. (2013a)
characterized the volatility distribution of I/S/L-VOC emissions into
different volatility classes. Recent studies have proven that considering
I/S-VOCs emitted from biomass burning shows a major improvement in the
agreement between the simulated and observed organic aerosol (OA)
(Ciarelli et al., 2017; Konovalov et al., 2015; Koo et al., 2014). Konovalov et al. (2015) find that ignoring I/S-VOCs
from biomass burning underestimates strongly the ratio of ΔPM10∕ΔCO (by a factor of 2) in the city of Kuopio
(Finland) and thus leads to an underestimation of the OA concentrations.

The objective of this study is to evaluate the capabilities of two CTMs to
simulate the impact of wildfires on the regional particulate matter budget.
In the Mediterranean region, surface PM10 is dominated by the
contribution from dust (Rea et al., 2015). Since the focus of this study is on
biomass burning, the discussion is centered on the simulation of surface
PM2.5. The total loading of aerosols over the region is evaluated using
comparisons of AOD to observations. After an evaluation of two CTMs (CHIMERE
and Polyphemus/Polair3D) through comparisons to observations from surface
networks and remote sensing, a sensitivity analysis of the injection heights
and I/S-VOC emissions is conducted in order to quantify the uncertainties
associated with these two parameterizations, in terms of AOD and surface PM
concentrations. This analysis is focused on the summer of 2007 which was
marked by extreme meteorological conditions (consecutive heat waves and
drought) and severe fire episodes in the eastern Mediterranean and Europe.
According to the European Forest Fire Information System (EFFIS), 2007 was
well above the average of the previous 3 decades in terms of burned area
(574 361 against 495 471 ha burned, respectively) (JRC, 2008).
The burning season was particularly severe for Greece, where the burned area
reached extreme values. This case study is particularly interesting since it
was well captured by satellite sensors as the resulting smoke plumes, fanned
by northeasterly winds, were transported over the sea, crossing the southern
Ionian Sea and reaching the northern part of the African continent. Several
studies have highlighted the important enhancements in atmospheric gases
(Coheur et al., 2009; Hodnebrog et al., 2012; Turquety et al., 2009) and aerosols (Kaskaoutis et al., 2011; Liu et al., 2009) due to these fire episodes. Modeling analyses have investigated
secondary production in the fire plumes. While Hodnebrog et al. (2012) showed
limited ozone impact on average during the summer, Poukpou et al. (2014)
investigated more precisely the fire event in the Peloponnese (Greece) at the
end of August 2007. They found enhancements of CO and NOx
concentrations mainly over the burnt areas due to the biomass burning. Due to
the nonlinear dependence of O3 on NOx levels, the
near-surface O3 values were reduced (−34 %) over the
Peloponnese, but increased (+52 %) over the sea at 500 km downwind.
Here, the sensitivities of regional aerosol loadings, both primary and
secondary, to modeling configurations are analyzed.

This paper is structured as follows. In the second section, a brief
description of the chemistry-transport models and the different tools
and methodology used in this work are given. Then, Sect. 3 presents
statistics for model-to-data comparison to assess the models' performance
during the summer of 2007 and more specifically during the main fire events.
Then, uncertainties related to the integration of wildfires in CTMs
(injection height, I/S-VOC emissions) are discussed in Sect. 4. Finally,
Sect. 5 describes the contribution of wildfires to air quality threshold
exceedances as well as the associated uncertainties.

2.1 Chemistry-transport models

Two CTMs are used for this study. CHIMERE simulations (Menut et al., 2013) allow
us to perform inter-model comparison and to evaluate the capability of other
current CTMs to simulate the impact of wildfires on the regional particulate
matter budget and to quantify the uncertainties on air quality modeling
related to the integration of fire emissions in CTMs. The sensitivity
analysis is undertaken using the Polyphemus modeling platform of air quality
(Mallet et al., 2007) with chemistry-transport model Polair3D
(Sartelet et al., 2012).

For the simulations presented in this work, Polyphemus is used with the
Carbon Bond 05 model (CB05) (Yarwood et al., 2005) for the gas-phase chemistry
(modified following Kim et al., 2011 for SOA formation) and with the SIze
resolved Aerosol Model (SIREAM) (Debry et al., 2007) for aerosol dynamics
(coagulation, condensation/evaporation). SIREAM uses a sectional approach and
the dry particle diameter is discretized with five sections between
0.01 µm and 10 µm. The simulations are performed on 14
vertical levels extending from the ground to 12 km.

Version 2016 of regional CTM CHIMERE is used for this work
(Mailler et al., 2017; Menut et al., 2013). Simulations were conducted using reduced chemical
mechanism Modele Lagrangien de Chimie de l'Ozone a l'echelle Regionale 2
(MELCHIOR2), which includes 44 species and 120 reactions, and the aerosol
module described in Bessagnet et al. (2004) (nucleation, coagulation,
absorption). This module is also based on a sectional representation of the
size distribution. For this simulation, 10 bins from 40 nm to
40 µm are used and simulations are performed on 19 vertical levels
extending from the surface to 250 hPa (σ-pressure coordinates).

Both models include wet and dry deposition. Deposition in Polyphemus is
described in Sartelet et al. (2007) and in CHIMERE in Menut et al. (2013) and
Mailler et al. (2017). Thermodynamics of inorganic aerosols are modeled using
ISORROPIA (Nenes et al., 1999) with a bulk equilibrium approach. Bulk equilibrium
is also used for SOA formation, and the partitioning between the gas and
particle phases is done with the Secondary Organic Aerosol Processor (SOAP)
(Couvidat and Sartelet, 2015) in Polyphemus. Photolysis rates are calculated using the
FastJX model (version7.0b for CHIMERE) (Wild et al., 2000). Their online
calculation in CHIMERE allows us to represent the attenuation by clouds and
aerosols, while the attenuation by clouds in Polyphemus is modeled by
multiplying the clear-sky photolysis rates by a correction factor
(Real and Sartelet, 2011).

Both models (Polyphemus and CHIMERE) are driven by meteorological conditions
simulated by the European Center for Medium-Range Weather Forecasts (ECMWF,
ERA-Interim) model. Initial and boundary conditions from MOZART4-GEOS5
6-hourly simulation outputs are used (Emmons et al., 2010). Simulations are
undertaken using two nested domains (Fig. A1 in
Appendix A). One large domain (0.5∘×0.5∘
horizontal resolution) covers Europe and northern Africa to provide realistic
dust sources and a smaller domain at 0.25∘×0.25∘
horizontal resolution over the Mediterranean area (presented in
Fig. 1).

Anthropogenic emissions are derived from the EMEP emissions inventory for
2007 (European Monitoring and Evaluation Program, http://www.emep.int,
last access: 20 January 2019). The
inventory species are disaggregated into real species using speciation
coefficients (Passant, 2002). The aggregation into model species is done
following Middleton et al. (1990). Primary particulate matter emissions are
given in total mass by the EMEP emission inventory. They are speciated into
dust, primary organic emissions (POA) and black carbon (BC), and distributed
into five diameter bins (Sartelet et al., 2007).

Biogenic emissions of isoprene and terpenes (α-pinene, β-pinene,
limonene and humulene) are calculated using the Model of Emissions of Gases
and Aerosols from Nature with the standard MEGAN LHIV database MEGAN-L for
Polyphemus and MEGAN v2.04 for CHIMERE (Guenther et al., 2006). Sea-salt emissions
are parameterized following Monahan (1986). The mineral dust emissions are
calculated using soil and surface databases (Menut et al., 2013) and with a
spatial extension of potentially emitting area in Europe as described in
Briant et al. (2017). The daily fire emissions are detailed in
Sect. 2.2.

In Polyphemus, I/S/L-VOC emissions are estimated by multiplying the primary
organic emissions (POA) by a factor of 1.5, following the chamber
measurements (Kim et al., 2016; Robinson et al., 2007; Zhu et al., 2016). The factor of 1.5 is used for
both anthropogenic and fire emissions to estimate the gas-phase I/S/L-VOCs
that are not included in the inventories. I/S/L-VOC emissions are assigned to
three surrogate species: POAlP, POAmP and POAhP (for compounds of low, medium
and high volatilities, respectively), of saturation concentration C*:
log10(C*)=-0.04, 1.93, 3.5, respectively. The volatility
distribution at emissions of I/S/L-VOCs is detailed in Couvidat et al. (2012)
(25 %, 32 %, and 43 % of I/S/L-VOCs are assigned to POAlP, POAmP,
and POAhP, respectively). It corresponds to the volatility distribution
measured by May et al. (2013a) for biomass burning aerosol emissions. Each primary
I/S/L-VOC undergoes one OH-oxidation reaction in the gas phase with a kinetic
rate constant equal to 2.10−11 molecule−1 cm3 s−1,
leading to the formation of secondary surrogates: SOAlP, SOAmP and SOAhP. The
ageing of the primary aerosols reduces the volatility of the secondary
product by a factor of 100 and increases the molecular weight by 40 %
(Couvidat et al., 2012).

2.2 Fire emissions

Daily fire emissions are calculated using the APIFLAME fire emissions' model
v1.0 described in Turquety et al. (2014). The carbon emission associated with a
specific fire is calculated using the MODIS burned area product at 500 m
resolution (MCD64 product) (Giglio et al., 2009), multiplied by the consumed fuel
load specific to the vegetation burned. The CORINE Land
Cover (CLC) is used for vegetation attribution,
and the biomass density is estimated based on simulations by the ORCHIDEE
vegetation dynamics and carbon cycle model (Maignan et al., 2009).

Turquety et al. (2014) estimated an uncertainty of ∼100 % on daily
carbon emissions using an ensemble approach. This is in agreement with
estimates for other daily inventories (e.g., GFED Van der Werf et al., 2010).

Emissions for each species are derived from the carbon emissions using the
emission factors from Akagi et al. (2011). These emission factors are provided in
terms of gram species per kilogram dry matter (DM) burned (g kg−1) for
all relevant species observed in biomass burning plumes and for different
standard vegetation types that match Mediterranean landscapes (chaparral,
temperate forest, crop residue, pasture maintenance and savanna). The
contribution of these vegetation types to the burned area detection over the
Mediterranean region during the time period studied is 37.2 % for
temperate forest, 32.7 % for savanna, 9.6 % for chaparral and
19.9 % for crop residue. Emissions for inventory species are then
converted into emissions for model species using model-specific aggregation
matrices (Yarwood et al., 2005). For aerosols, the difference between emissions
factors provided for the total PM2.5 and for the main primary emissions
(organic and black carbon, small amounts of inorganics) is modeled as a
specific, inert and unidentified species grouping the remaining mass of
PM2.5 (REM-PM2.5). REM-PM2.5 corresponds to all the
unidentified fine particles emitted by wildfires which are incorporated to
consider the differences between PM2.5 emissions and the total of all
PM2.5-speciated emissions. The difference between PM10 and
PM2.5 emission factors is attributed to emissions of coarse-mode PM
(PPMcoarse).

Figure 1 shows a map of total primary organic carbon emissions
(OC) from fires in the Euro-Mediterranean region during the summer of 2007.
Four main areas are affected by wildfires: Balkans (sub-region MedReg1),
Greece (sub-region MedReg2), southern Italy (sub-region MedReg3) and Algeria
(sub-region MedReg4).

Figure 1Map of the nested domain over the Mediterranean area with a spatial
resolution of 0.25∘×0.25∘. The total organic carbon
emissions (kg (grid cell)−1) from fires during the summer of 2007 are
presented. The sub-regions used in this study are also indicated in colored
boxes: MedReg1 (Balkans + eastern Europe), MedReg2 (Greece), MedReg3
(Italy) and MedReg4 (Algeria).

Total daily fire emissions for the four studied areas are plotted in
Fig. 2. In all regions, fire emissions are occasional but very
intense. The largest fires in the simulated domain occur in the Balkans
(sub-region MedReg1) between 20 July and 31 July 2007, and in Greece
(sub-region MedReg2) between 24 and 30 August. In Algeria (sub-region
MedReg4), fires mainly occur at the end of August and beginning of September
(28 August–1 September). In southern Italy (sub-region MedReg3), fires are
observed between 9 and 31 July 2007.

Figure 2Daily total OC emissions calculated by APIFLAME during the summer of
2007 in the four sub-regions of Fig. 1.

2.3 Model sensitivity experiments

Two different parameters, considered critical for modeling fire events, are
tested using sensitivity simulations with the Polyphemus CTM: fire emissions
of I/S-VOCs and emissions' injection heights. Another simulation is also
conducted to evaluate the impact of REM-PM2.5 emissions. In the
simulations discussed here, four different configurations of the model are
used.

Simulation Poly-ref: fire emissions are homogeneously mixed in the planetary boundary layer (PBL), but no lower than 1 km.
The percentage of fire emissions' injection is divided homogeneously
depending on the layers' thickness (I/S/L-VOCs included).

Simulation Poly-noI/S-VOCs: fire emissions in the PBL but without I/S-VOC emissions in the gas phase. In this sensitivity
study, the semi-volatile properties of POA are not considered, and POA emissions are modeled as LVOCs.

Simulation Poly-NoREM-PM2.5: fire emissions in the PBL, with I/S/L-VOCs but without REM-PM2.5.

Simulation Poly-3 km: fire emissions injected up to 3 km with: 20 % under 1 km, 80 % between 1 and 3 km.
Note that, in this case, 78 % of fire emissions are injected above the PBL. This choice of sensitivity study may be viewed as
conservative since, for example, injection heights are limited to 3 km. But it is also extreme since maximum injection at 3 km is
imposed to all fires, resulting in injection above the boundary layer. This could be realistic, since based on the Multi-angle Imaging
SpectroRadiometer (MISR) observations, Mims et al. (2010) estimated that 26 % of the fire plumes exceed the boundary layer.

In addition, a simulation with the CHIMERE model (without I/S-VOCs and with
fires in the PBL) (CHIMERE-ref) allows an inter-model comparison.
Simulations without fires are also computed with both models
(Poly-Nofires and CHIMERE-Nofires). The setup of the
different simulations is summarized in Table 2.

Table 2Summary of the configurations used in different simulations. n/a –
not applicable.

3.1 Observations

Daily observations from European network AIRBASE 5 are used for PM
concentrations. Due to the relatively coarse horizontal resolution, only
background stations are included in the present paper. The investigated
stations are indicated in Fig. 3. PM10 concentrations in the
Mediterranean area are strongly affected by dust, which are difficult to
simulate due to their sporadic nature and the fact that their main sources
are located out of the model domain. Although dust emission is modeled with
state-of-the-art parameterization in this study, the analysis focuses on
PM2.5 in order to evaluate more specifically the uncertainties
associated with wildfires and to minimize the contribution from dust.

Surface observations are complemented by remote sensing observations of
aerosol optical properties. AOD level 2.0 data (at 550 nm) and Angstrom
coefficient α (derived from AOD at 500 and 870 nm) from the AERONET
(AErosol RObotic NETwork) ground-based network of sun photometers
(Holben et al., 1998) are used. The uncertainty on AOD is estimated to less than
0.02 (Holben et al., 2001). AOD level 2.0 observations are missing in Lecce
University (in Italy) during the first event, and in Blida (in Algeria)
during the second one. Since AOD Level 1.5 observations at 500 nm (before
filtering) are available for the latter, we have chosen to include these
observations for comparison in the next sections. The considered stations are
indicated in Fig. 3.

AOD observations at 550 nm from the MODIS instrument on board the Terra
(Equator crossing time at 10:30, ascending node) and Aqua (Equator crossing
time at 13:30, ascending node) satellites are also used in order to get a
more complete regional view. The MOD04 and MYD04 (for Terra and Aqua,
respectively) from collection 5.2 data, available at 10×10 km2,
are used (Remer et al., 2005). The expected uncertainty on AOD is Δτ=±0.05±0.15τ (τ is the optical thickness) over land (Chu et al., 1998; King et al., 1999) and Δτ=±0.03±0.05τ over the ocean (King et al., 1999),
with a good agreement with ground-based measurements (Remer et al., 2005). Deep
Blue AOD (Sayer et al., 2013) is used when available (over bright areas).

3.2 Comparison method

A set of statistical indicators are used for the comparison of model
simulations to surface measurements: the root mean square error (RMSE), the
correlation coefficient (R), the mean fractional bias (MFB) and the mean
fractional error (MFE). These are defined as

with oi the observed concentration at time and location i, ci the modeled concentration at time and location i, and n the number of data.
Boylan and Russel (2006) proposed for PM that a model performance criterion (level of
accuracy acceptable for standard modeling applications) is met when MFE
≤+75 % and MFB is within ±60 %, and a model performance
goal (level of accuracy considered to be close to the best a model can be
expected to achieve) is met when MFE ≤50 % and MFB is within
±30 %. In the following, the MFB and MFE are computed at each
station and averaged.

3.3 Overview of the 3-month period

The statistical evaluation of the simulations during the summer of 2007 (from
15 July to 30 August 2007) is presented in Table 3 for
PM2.5 concentrations and Table 4 for AOD at 550 nm.
Globally, the PM2.5 concentrations and AOD are well reproduced by the
models, although they are slightly underestimated compared to measurements.
For AOD, the model performance and goal are always met. For PM2.5
concentrations, the model performance is always met, and the model goal is
met for the model errors' MFEs. The models-to-measurements correlations range
between 46 % and 83 % for all the simulations when fires are
included. The model errors' MFEs are similar for outputs from CHIMERE and
Polyphemus. However, CHIMERE outputs have lower bias (higher concentrations
are AOD values), while Polyphemus outputs have higher correlations. The
models-to-measurements comparisons tend to improve when fires are taken into
account, with lower MFEs and higher correlations. The improvement is stronger
for AOD than for PM2.5 concentrations, because the stations used for AOD
models-to-measurements comparisons are closer to regions affected by
wildfires than the stations used for PM2.5 models-to-measurements
comparisons, as discussed below.

Figure 3 shows the mean surface concentrations of PM2.5
simulated by Polyphemus (Poly-ref) over the Euro-Mediterranean
domain, from 15 July to 30 August 2007. The eight AIRBASE stations used for
the models-to-measurements comparison are also presented in Fig. 3.
The mean simulated PM2.5 concentrations can reach
60 µg m−3 in regions affected by wildfires (Balkans,
sub-region MedReg1, and Greece, sub-region MedReg2). Only two AIRBASE
stations, GR0039A in Greece and IT0459A in Italy, are close to regions
affected by wildfires. Figure B1 in Appendix B shows that
conclusions for CHIMERE are similar to those for Polyphemus.

Figure 3 also shows the mean modeled AOD, which can be as high as
0.72 on average in the Balkans and in Greece, and the six AERONET stations
used for the models-to-measurements comparisons. Five out of the six stations
are located in regions affected by wildfires.

This evaluation shows good general performance of the models and a clear
improvement of PM2.5 and AOD when fires are included, allowing a more
precise analysis of the model's behavior during the strongest fire events.

Table 3Statistics of models-to-measurements comparisons for the mean daily
PM2.5 concentrations during the summer of 2007 (AIRBASE station
number =8).

Figure 3Daily mean surface PM2.5 and AOD at 550 nm from the
Poly-ref simulation averaged over the summer of 2007 (the eight
AIRBASE and six AERONET stations, used in this work, are represented here in
blue dots).

3.4 Fire events

To better understand the sensitivity of PM2.5 concentrations and AOD to
I/S-VOC emissions and the fire injection heights during the fire events, the
composition of PM during fire peaks and the evaluation of fire contribution
are examined. The locations of the fire peaks during the two main fire events
are first detailed.

3.4.1 Locations of the fire peaks

The contribution of fires to PM2.5 concentrations simulated by
Polyphemus during the two main fire events (20–31 July and 24–30 August) is
presented in Fig. 4 as the relative difference between the
reference simulation (Poly-ref) and the simulation without fire
emissions (Poly-Nofires). The largest contribution is simulated over
the Balkans and eastern Europe during the first period (sub-region MedReg1)
and Greece (sub-region MedReg2) and Algeria (sub-region MedReg4) during the
second period. The impact reaches up to 90 % locally on average during
each fire event. The contribution of fires remains large (>60 %) over
most of the eastern Mediterranean basin and part of the western basin at the
end of August due to long-range transport of fire plumes. Figure C1
in Appendix C shows that the contribution of fire for CHIMERE is
similar to that for Polyphemus. Only the closest AIRBASE and AERONET stations
to fire regions (where fire contribution is higher than 50 %) are used in
models-to-measurements comparisons in the next sections. During the first
fire event, the most affected stations are GR0039A in Greece (sub-region
MedReg2) and Bucharest in Romania (sub-region MedReg1), while during the
second fire event, stations in Greece (Thessaloniki in sub-region MedReg2),
Italy (Lecce University in sub-region MedReg3) and Algeria (Blida in
sub-region MedReg4) are the most influenced. However, since the models show
the same behavior during the second fire event over stations GR0039A and
Thessaloniki, we choose to focus only on GR0039A.

Figure 4(a) Relative difference of surface PM2.5
concentrations between simulations Poly-ref and
Poly-Nofires during the first fire event. (b) Relative
difference of surface PM2.5 concentrations between simulations Poly-ref and Poly-Nofires during the second fire
event.

3.4.2 Aerosol composition during fire peaks

The compositions of surface PM2.5 concentrations simulated during the
first fire event over MedReg1 and during the second one over MedReg2 are
shown in Figs. 5 and 6, respectively. These two
subregions are the areas most affected by fires (high fire emissions
(Fig. 1) especially during the first fire event for MedReg1 and
during the second fire event for MedReg2). The upper left panel shows the
composition of surface PM2.5 concentrations for the simulation without
fire Poly-Nofires (background surface PM2.5 concentrations),
while the upper right panel shows the composition of surface PM2.5
concentrations due to fires (differences between simulations
Poly-ref and Poly-Nofires). If wildfires are not taken into
account (simulation without fire) and during the first fire event over
MedReg1, organic and inorganic aerosols contribute equally (42.6 %,
40.5 %) to the surface PM2.5 concentrations. The contributions of
REM-PM2.5 (dust) black carbon are lower (15 %, 1 %). As noted by
Chrit et al. (2017), most of the summer organic aerosols are from biogenic
sources in this region. If wildfires are not taken into account during the
second fire event over MedReg2, inorganics and REM-PM2.5 are the
predominant component in the composition of PM2.5 concentrations
(56.5 % and 27.9 %). Lower contributions are simulated for black
carbon (1.2 %) and organic aerosol (14.3 %). Figures 5
and 6 also show the composition of surface PM2.5
concentrations due to fires for simulations Poly-ref (differences
between simulations Poly-ref and Poly-Nofires),
Poly-noI/S-VOCs (differences between simulations
Poly-noI/S-VOCs and Poly-Nofires), and
Poly-NoREM-PM2.5 (differences between simulations
Poly-NoREM-PM2.5 and Poly-Nofires). During the first
fire event over MedReg1, organic aerosol is predominant in the contribution
of fires (between 47 % and 85 % of the contribution). Organic aerosol
is mostly composed of POA and SOA from I/S/L-VOCs (46 % to 80 %).
Note that POA and SOA from L-VOCs (low volatile organic compounds) are
important even in the simulation when I/S-VOCs are not taken into account in
fire emissions (46 %), because POA are then assigned to L-VOCs. The
contributions from inorganics (8 % to 13 %) and black carbon (3 %
to 6 %) are low. During the second fire event over MedReg2, a similar
PM2.5 composition is found. Organic aerosol (mainly the POA and SOA from
I/S/L-VOCs) is the most important component contributing to PM2.5 from
fires (between 46 % and 81 % of the contribution). The contributions
from inorganics (9 % to 12 %) and black carbon (5 % to 6 %)
are lower. The contribution of REM-PM2.5 from fires (if it is included)
is very significant: 27 % for the simulations with I/S-VOCs and 36 %
otherwise. Because REM-PM2.5 emissions are incorporated to consider the
difference between PM2.5 emission factors and the total of all PM
included in specific species, the contribution of REM-PM2.5 may be
overestimated (double counting with I/S-VOCs for instance).

In our study, inorganics (mainly sulfate, sea salt and ammonium) contribute
highly to PM2.5 composition, if fire emissions are not considered.
Similar results are found in Fountoukis et al. (2011) who showed the high
contribution of sulfate, sea salt and ammonium to PM over Europe during May
2008. However, when fire emissions are taken into account, the contribution
of inorganics becomes lower than the contribution of organics (8 % to 9 %
of inorganics against 40 % to 80 % of organics). Focusing on the
contribution from fires, sulfate, ammonium and nitrate are the predominant
components in the composition of inorganics from fires: between 55.7 %
and 67.6 % for sulfate, between 26.8 % and 38.7 % for ammonium and 5.6 %
to 13.6 % for nitrate.

Similar PM2.5 composition is found during the second fire peak, and in
the concentrations simulated by CHIMERE (not shown here). Surface PM2.5
concentrations from fire simulated by CHIMERE are composed in the first and
second events mainly of organic aerosols, mostly composed by primary organic
carbon (OCAR) (46 %) which corresponds to I/S/L-VOCs in Polyphemus, and
of REM-PM2.5 (39 %). The contributions from inorganics (9 %),
black carbon (5.2 %) and SOA (2.7 %) are low.

3.4.3 Evaluation of fire contribution

Figure 7 shows time series of daily observed and simulated aerosols
at background suburban and background rural stations with available data
during fire events and with a fire contribution higher than 10 %
(PM2.5 in Greece (sub-region MedReg2), AOD at 550 nm in Italy
(sub-region MedReg3), Romania (sub-region MedReg1) and Algeria (sub-region
MedReg4). A significant increase in AOD and PM2.5 concentration is
observed during the major fire episodes on 20–28 July and 24–30 August,
associated with large contributions from fires. The daily average AOD,
observed by MODIS and simulated for the sub-regions of Fig. 1, are
shown in Fig. 8. Background AOD and daily variability are
consistent with MODIS for both models in the sub-regions of
Fig. 1, with high correlation coefficients (∼90 % on
average for Polyphemus, 80 % for CHIMERE).

As shown in Figs. 7 and 8, injecting emissions
higher (simulation Poly-3 km) significantly lowers surface PM
concentrations (compared to simulation Poly-ref), even if the
maximum injection height remains conservative. Not taking into account SOA
from I/S-VOCs directly reduces emissions, thus having a strong influence on
PM concentrations (−20 % compared to Poly-ref). A reduction of
the same order of magnitude is obtained when
REM-PM2.5 is not accounted for (Poly-NoREM-PM2.5), suggesting that the
incorporation of this species into APIFLAME could compensate for the missing
I/S-VOC emissions.

Compared to MODIS AOD (Fig. 8), the simulations including
I/S-VOCs (Poly-ref, Poly-3 km and
Poly-NoREM-PM2.5) overestimate AOD during the fire events,
while simulations without I/S-VOCs underestimate AOD. This is more pronounced
in the two sub-regions MedReg1 and MedReg2, where the mean modeled AOD values
are overestimated by about 30 % for Poly-ref and
Poly-3 km and 10 % for Poly-NoREM-PM2.5. Compared
to AIRBASE ground measurements (Fig. 7), the peak corresponding to
the first event (25 July) is well modeled in simulation Poly-ref
compared to PM2.5 observations at GR0039A (Athens suburbs, Greece,
sub-region MedReg2). The temporal variations of the mean simulated PM2.5
concentrations are consistent with observations, with high correlation
coefficients (>88 %). Background levels of PM2.5 are slightly
underestimated compared to observations. This can be explained by an
underestimation of dust long-range transport or an underestimation of local
emissions. The peak of PM2.5 concentration is slightly underestimated in
all the other simulations. At the GR0035A station, the temporal tendencies of
the simulated PM2.5 concentrations are consistent with the observations.
However, the first PM2.5 peak is underestimated since PM22.5 levels
are strongly underestimated (bias =-85 %). The observed background
levels are significantly higher than those at the GR0035A station. This
suggests the influence of local emissions that are missing in the model or
limitations due to the coarse model resolutions which can not represent this
station. At Bucharest, two high AOD peaks are modeled (Fig. 7).
Compared to AERONET ground observations, the first peak is well modeled on
23 July (0.46 against measurements 0.5) and underestimated on 30 July (0.51
against measurements 0.82 but simulated 1 day after the observations,
probably due to uncertainty in the MODIS fire detection). The observed values
of the Ångström exponent are lower on 23 July (α∼ 0.57),
indicating a large fraction of coarse-mode particles (probably dust
transport), than on 30 July (α∼ 1.6, large fraction of fine-mode
particles from fires). These underestimations or overestimations of the
model's AOD, depending on the data set used for the evaluation (AERONET vs.
MODIS), underline uncertainties in AOD retrievals from measurements, which
have already been observed by numerous studies (Boiyo et al., 2017; Li et al., 2009; Wu et al., 2017).

During the second fire event, the contribution from fires becomes predominant
on 24 August (beginning of the event according to MODIS fire observations).
The best agreement with PM2.5 in Greece is obtained for simulations
Poly-3 km and CHIMERE, although the latter overestimates surface
concentrations on the following days. At the GR0035A station, the second
observed peak during the second fire event is more than twice as high as the
observed peak at GR0039A and is measured 1 day earlier. In the simulations,
the enhancement due to fires is similar in shape and magnitude, clearly
highlighting the difficulty in simulating the exact temporal variability of
emissions and transport of fire plumes in CTMs. Moreover, station GR0035A is
probably affected by a dust episode during the second fire event. For AOD at
Lecce University, all simulations show good agreement with observations.
According to AERONET level 1.5 measurements in Blida (in Algeria, sub-region
MedReg5), an AOD peak (0.55) is observed on 27 August. The AOD simulated by
CHIMERE and Polypohemus is consistent with the measurements mainly for
Poly-ref, Poly-3 km, Poly-NoREM-PM2.5 and
CHIMERE-ref. Poly-noI/S-VOCs shows the lowest AOD values at
Lecce University (0.38) and Blida (0.33). This suggests that taking into
account I/S-VOC emissions leads to higher and more realistic AOD at these
stations.

In Blida, three peaks are simulated for 2, 6 and 16 August. The observed
values of the Ångström exponent are equal to α∼1.18 and
α∼1.14 for the first and third peaks, respectively, which
indicates fine-mode particles. Therefore, the first and third peaks are
attributed mostly to fires in Algeria on 2 and 16 August. However, emissions
are probably underestimated, as all the models underestimate the AOD fire
peaks. Since a lower value of the Ångström exponent is observed on 6
August (α∼ 0.91), this second peak is probably attributed to dust.

This analysis highlights the strong regional impact during intense events on
both AOD and PM2.5 concentrations but also the difficulties in
representing their amplitudes and variability. Considering the uncertainty on
fire data and emissions (∼100 %) and on the observations
(1 %–2 % for AERONET observations, Eck et al., 1999, and
∼34 % for AIRBASE observations, Bovchaliuk, 2013), the
performance obtained is considered very reasonable. The spread in the
different model configurations tested shows the additional uncertainty on the
modeling of fire impact. Observations generally lie within the simulated
variability, but it is difficult to extract the best model configuration (it
depends on the event and on the station). For the first fire event,
Fig. 9 shows maps of the daily mean AOD at 550 nm from
MODIS, modeled by Polyphemus (Poly-ref) and
CHIMERE (CHIMERE-ref). The simulated AOD is generally in good
agreement with the observations in terms of localization. The AOD calculated
from Poly-ref and CHIMERE-ref simulations is close to
observations in the Balkans. However, it seems overestimated in the fresh
plume and further downwind (reaching ∼0.65 for Poly-ref and
0.52 for CHIMERE-ref). Results of Poly-noI/S-VOCs are close
to those of the CHIMERE model, which does not include I/S-VOC emissions.
During the second fire event in Greece, the simulated AOD for
Poly-ref and for CHIMERE-ref is about 1 and 0.9,
respectively, as shown in Fig. D1 in Appendix D. The
observed AOD can reach 0.9. However, both models overestimate AOD values in
the fire plume (reaching 0.98 for Poly-ref and 0.88 for
CHIMERE-ref against 0.7–0.8 in Greece and 0.5 from MODIS). The fire
plume is less pronounced in observations than in simulations.

Day-to-day comparisons for 4 selected days (24, 25, 27 and 29 August 2007)
are shown in Fig. E1 in Appendix E. The simulated AOD is
consistent with the observations in terms of localization and general
transport pathways. However, the simulated AOD is much higher in the Greek
fires' plume compared with MODIS observations during the peak of emissions
(25–29 August). This probably reflects too low a temporal variability in the
emissions. In the simulations, emissions are assumed constant during the day,
but comparisons suggest shorter temporal variability. This is also apparent
in the time series of Fig. 8, over MedReg2: the peak for
the second fire event is twice as long in the simulations (double peak
starting on 25 August 2007) as in the observations. This peak over 2 days
instead of 1 in the simulations suggests an overestimate of emissions during
this event which is also observed with respect to surface observations in
Greece. The first peak corresponds better to observations. This shows that
uncertainties are related not only to total emissions, but also to their
temporal variability and the associated transport pathways.

Figure 7Time series from 15 July to 30 August of daily mean surface
PM2.5 concentrations at AIRBASE station GR0039A and GR0035A daily mean
AOD
at 550 nm at three AERONET stations (Lecce University, Bucharest, Blida). The red triangles in Blida station correspond to
AERONET measurements using AOD level 1.5 AOD data at 500 nm. Statistics for simulation Poly-ref are shown at each station.

The sensitivity of the modeling of I/S-VOC emissions and injection heights on
simulated surface PM2.5 concentrations and AOD is now evaluated
regionally over the Mediterranean domain.

The sensitivity of the model results to the I/S-VOC emissions and injection
heights is compared to the inter-model sensitivity presented in
Fig. 10, which shows the relative differences between a sensitivity
simulation (CHIMERE-ref, Poly-noI/S-VOCs,
Poly-3 km) and the reference simulation Poly-ref. To focus
on fire impact, only the PM2.5 concentrations and AOD exceeding
15 µg m−3 and 0.25, respectively, are taken into account when
computing the relative differences between the simulations. It is worth
noting that the arbitrary choice made in this work (injecting between 1 and
3 km) may overestimate the impact of injection height on surface PM2.5
concentrations and underestimate it on long-range transport (injecting fire
emissions at or below 3 km remains conservatively low).

The inter-model sensitivity is low (relative differences below 20 %) for
both surface PM2.5 concentrations and AOD, except in the Balkans
(sub-region MedReg1), where it can reach 50 % locally. The high
inter-model differences are slightly more spread horizontally for surface
PM2.5 concentrations than for AOD. Furthermore, this region of high
inter-model sensitivity corresponds to the region where the sensitivity to
the injection height is highest for PM2.5 concentrations. It may
therefore be linked to differences in the models' vertical discretization.
The models use different vertical coordinates and different numbers of
vertical levels. The vertical resolution of the models is rather low as
Polyphemus uses 14 vertical levels and CHIMERE uses 19 vertical levels.

Neglecting I/S-VOC emissions leads to a decrease in surface PM2.5
concentrations. The impact of I/S-VOC emissions on surface PM2.5
concentrations over the fire regions is mostly under 20 %, but reaches
30 % locally. The impact of I/S-VOC emissions is spread over larger areas
than the inter-model difference.

I/S-VOC emissions have a higher impact on AOD than on surface PM2.5
concentrations, since adding I/S-VOCs increases the total integrated
PM2.5 concentrations: the increase can be as high as 40 % in the
Balkans and 30 % in Greece and the fire plume.

As previously discussed, a surrogate species may be considered to represent a
missing mass in the model fire emissions, REM-PM2.5 in this study
(emissions factors of PM2.5 minus primary aerosol emissions of OM, BC
and sulfate). This missing mass may be used to fill a gap in current
evaluation of emissions. However, considering REM-PM2.5 emissions and
I/S/L-VOC emissions may be redundant. While a fraction of
REM-PM2.5 can be due to the low diameter of dust particles, several
models partly treat this missing mass by deducing I/S/L-VOC contribution from
particulate matter emissions (Konovalov et al., 2015; Koo et al., 2014). For example, several
studies estimated the contribution from I/S/L-VOC emissions by multiplying
the primary organic aerosols (POA) by a factor of 1.5 following chamber
measurements (Kim et al., 2016; Robinson et al., 2007; Zhu et al., 2016). Some studies/models do not
consider specific species/surrogates to treat these missing emissions, but
simply use a ratio to reduce uncertainties related to the estimation of PM
emissions. For example, Kaiser et al. (2012) use a factor of 3.4 for PM emissions
based on the comparison between simulations and AOD observations.

Injecting above the boundary layer results in larger-scale transport for
PM2.5 concentrations that leads to the highest impact on surface
PM2.5 concentrations (40 % to 50 % near the fire regions and
30 % in the fresh plume and further downwind). However, the impact of the
injection height on AOD is lower but still significant (mostly under
20 %, but reaching 30 % locally). Similar results are found in
Turquety et al. (2007), Chen et al. (2009), Stein (2009), Daskalakis et al. (2015),
and Gonzi et al. (2015). In fact, previous studies highlighted the high
sensitivity of long-range transport of carbon monoxide (CO) to wildfire
injection height (Gonzi et al., 2015; Turquety et al., 2007). It also leads to a reduction of
concentrations at the surface Chen et al. (2009). The sensitivity analysis of
Stein (2009) estimates a strong reduction in the surface PM2.5
concentrations (>10µg m−3) caused by fire emission
injection height over the United States. According to Daskalakis et al. (2015),
assumptions about the injection heights of fire emissions can also result
regionally in up to 30 % differences in the calculated tropospheric
lifetime of pollutants. This can lead to significant interactions between
isoprene and fire emissions. Daskalakis et al. (2015) showed that these
interactions affect the effectiveness of isoprene in producing secondary
aerosols (up to 18 %).

This analysis highlights that injecting above the boundary layer is more
critical for surface PM2.5 concentrations than integrating I/S-VOC
emissions, since 78 % of fire emissions are emitted above the boundary
layer. However, for AOD and vertically integrated concentrations, integrating
I/S-VOC emissions is more critical than the injection heights.

Figure 10Sensitivity of surface PM2.5 concentrations (a, c, e) and
AOD at 550 nm (b, d, f) to the CTM used (CHIMERE-ref or Poly-ref, upper panels), the
modeling of I/S-VOC emissions (Poly-noI/S-VOCs
or Poly-ref, middle panels) and the injection height (IH) (Poly-3 km or Poly-ref, lower panels) during
the summer of 2007 (15 July to 30 August). Simulation Poly-ref is
used in all panels to estimate the relative differences.

The ensemble of the sensitivity simulations provides an estimate of the
uncertainty of the modeling of fire plumes. To quantify uncertainties related
to fire emissions modeling, the maximum statistical dispersion (σ) is
used as a statistical estimator. The maximum statistical dispersion
(σ) for surface PM2.5 and AOD calculated as

(5)σ=1N∑i=1N(Xi-ΔX‾)2ΔX‾.100,(6)ΔX‾=1N∑i=1N(Xi),

with X refers to either surface PM2.5 concentrations or AOD; N is
the total number of the simulations with fire emissions included (N is
equal to 4).

Figure 11 shows the average surface PM2.5 concentrations and
AOD estimated using four Polyphemus simulations and the maximum statistical
dispersion (σ) for surface PM2.5 concentrations and AOD. The
maximum statistical dispersion related to simulated surface PM2.5
concentrations and AOD is higher near the fire regions. The mean surface
PM2.5 concentrations estimated from the four Polyphemus simulations can
reach 42 µg m−3 in the Balkans and Greece, with a statistical
dispersion that can be as high as 75 %. Lower mean surface PM2.5
concentrations are calculated for Algeria, in the fresh fire plume and
further downwind (28 µg m−3 with a maximum statistical
dispersion reaching 45 %). The mean AOD estimated as the average of four
Polyphemus simulations can reach 0.42 in the Balkans and Greece, and 0.36 in
the fire plume with a statistical dispersion that can reach 45 % and
36 %, respectively.

Although these uncertainties are quite high, they are probably still
underestimated since several other sources of uncertainties should be
considered. First of all, uncertainty in the initial emissions is important.
Uncertainty in the burned area and the associated temporal evolution (used as
input to the calculation of emissions) is also high. Giglio et al. (2010) found
that uncertainties on MODIS observation can reach about 5 days mainly due to
cloud cover.

Although the contribution from organic aerosols is dominant, biomass burning
is also a source of inorganics. Over Europe, inorganics (mainly sulfate, sea
salt and ammonium) contribute highly to PM2.5 composition, when fire
emissions are not considered (e.g., Fountoukis et al., 2011;
Chrit et al., 2018). However, during fire events the contribution of
inorganics is lower than the contribution of organics (8 % to 9 %
from inorganics against 40 % to 80 % from organics). Focusing on this
inorganic contribution from fires, sulfate, ammonium and nitrate are the
predominant inorganic components. The formation of inorganics due to
wildfires is found to be low compared to the formation of organics. However,
our simulation takes into account emissions of inorganic precursors such as
ammoniac (NH3) with emission factors from Akagi et al. (2011). Several
studies (R'honi et al., 2013; Van Damme et al., 2014; Whitburn et al., 2017) show that large emissions of
NH3 are released by biomass burning. Whitburn et al. (2017) studied the
enhancement ratios NH3∕CO for biomass burning emissions in the
tropics using observations from the IASI satellite-based instrument. They
found a significant variability due to fire contribution. According to
Whitburn et al. (2017), the emission ratios NH3∕CO in the tropics
derived from IASI observations (as in Van Damme et al., 2014) are rather on the
lower end of those reported in Akagi et al. (2011) that are used here. If fire
emissions are important for the regional budget of organics, more
observations are required to provide emission factors of NH3 and
concentrations of inorganics should be evaluated close to fire regions.

Deposition can be considered a source of uncertainty on PM and AOD over the
Euro-Mediterranean region during the summer of 2007. Roustan et al. (2010)
pointed out the importance of dry and wet deposition over Europe while
studying the sensitivity of Polyphemus to input data over Europe with a focus
on aerosols. They found that PM is sensitive to options influencing
deposition such as wet diameter and aerosol density. They found that during
both summer and winter, the uncertainties on wet diameter and aerosol density
can reach 19 % and 9 %, respectively. Several studies also highlight
the importance of the gas-phase deposition of I/S/L-VOCs (Bessagnet et al., 2010; Hallquist et al., 2009; Hodzic et al., 2016; Karl et al., 2010; Knote et al., 2015). Hallquist et al. (2009) highlighted
the importance of the vapor deposition which is higher than the particle
deposition (800 vs. 150 TgC yr−1).
Knote et al. (2015) showed that the gas-phase I/S/L-VOCs that are highly water
soluble (Henry's law constants H∗ between 105 and
1010 M atm−1) are very susceptible to removal processes in the
atmosphere (wet deposition and dry deposition). Ignoring the removal of
gas-phase I/S/L-VOCs (dry/wet deposition) in the models can lead to
uncertainties on SOA concentrations, AOD and PM2.5 concentrations.

Meteorological conditions also play an important
role in pollution dispersion and the capabilities of models to reproduce
observed pollution plumes. Garcia-Menendez et al. (2014) found that simulated
PM2.5 concentrations at urban sites displayed large sensitivities to
wind perturbations within the error range of meteorological inputs.
Rea et al. (2016) added that special attention must be paid to the PBL height,
which can have a considerable impact on the fire emissions' injection
heights. Therefore an assessment of uncertainties related to meteorological
data should be investigated.

Air quality impact is evaluated by analyzing the number of threshold
exceedances during the summer of 2007. It corresponds to the number of times
daily averaged PM2.5 surface concentrations exceed
25 µg m−3 (World Health Organisation recommendation,
Krzyzanowski and Cohen, 2008).

The number of exceedances predicted by the model is first compared to
exceedances observed by AIRBASE stations. Table F1 in
Appendix F presents the modeled and observed PM2.5 exceedances
at each AIRBASE station during the whole summer of 2007 and during fire
events. Generally, the models (Polyphemus and CHIMERE) underestimate
PM2.5 air quality exceedances because the horizontal resolution used
here does not allow the representation of local pollution (especially for
station GR0035A). Better performance is observed during fire events than the
whole period. Near fire regions, at station GR0039A, the number of
exceedances is well modeled, in spite of the slight underestimation during
both fire events compared to the observed PM2.5 exceedances. However, at
the GR0035A station, the underestimation of PM2.5 exceedances is sharp.
This can be explained by the strong underestimation of the background
PM2.5 levels as shown in Fig. 7. Far from fire regions, the
PM2.5 exceedances modeled by Polyphemus are in good agreement with the
observed ones, especially during the two fire events. The simulated
PM2.5 exceedances by CHIMERE in the fire plume are overestimated at some
stations. Figure 12 shows the additional days
with PM2.5 exceedances due to fires simulated by Polyphemus (difference
between Poly-ref and Poly-Nofires). Most are concentrated
around fire sources, mainly in the Balkans (30 days). Figure G1 in
Appendix G shows that conclusions for CHIMERE are similar to those
for Polyphemus. Fires cause up to 49.5 % of the total simulated
exceedances from 15 July to 30 August 2007.

Figure 12The additional days with PM2.5 exceedances due to fires
simulated by Polyphemus (difference between Poly-ref and
Poly-Nofires) (a) and the maximum statistical dispersion
(σ) related to fire emissions for additional days of PM2.5
exceedances due to fires (b) during the summer of 2007 (from 15 July
to 30 August 2007).

During the fire periods, surface PM2.5 concentrations simulated in
Poly-ref in the MedReg1 and MedReg2 sub-regions are composed mainly
of OM (54 % with 61.6 % due to fire for MedReg1 and 52.3 % with
60.1 % due to fires for MedReg2), and inorganics (27 % with 8 %
due to fires for MedReg1 and 15 % with 9 % due to fires for MedReg2).

Figure 12 shows that the maximum statistical
dispersion on the simulation of PM2.5 exceedances can reach 15 days in
regions affected by fires, particularly 5 days in GR0039A, 12 days in the
fire plume and 6 days further downwind, based on our ensemble of sensitivity
simulations.

A sensitivity analysis was undertaken to estimate the uncertainty of the
parameterization of wildfire emissions into air quality models over the
Euro-Mediterranean region during the summer of 2007. The ability of
Polyphemus/Polair3D and CHIMERE to simulate regional surface PM2.5
concentrations and aerosol optical depth (AOD) was evaluated based on
comparisons to available measurements. The comparison of surface PM2.5
concentrations and AOD at 550 nm to available surface measurements
(background suburban and rural stations only) shows that both models meet the
performance criteria. A clear improvement is noticed when biomass burning
emissions are taken into account.

The contribution of biomass burning is large in the Balkans and eastern
Europe during the first fire period (20–31 July) and Greece and Algeria
during the second period (24–30 August). According to the simulations,
PM2.5 close to regions affected by fires is mostly composed of organic
aerosol (47 % to 85 %), with a strong contribution from I/S/L-VOCs
(46 % to 80 %). However, only two AIRBASE stations (GR0039A and
GR0035A in Greece) and three AERONET stations (Lecce University in Italy,
Blida in Algeria and Bucharest in Romania) show a fire contribution higher
than 10 % according to the model simulations. The lack of surface
observations strongly limits this evaluation, but it is partly complemented
by comparisons to MODIS satellite-based observations of AOD. Comparisons to
satellite observations over subregions show a good simulation of the daily
variability of AOD, with high correlation coefficients for Polyphemus
(∼90 % on average) and CHIMERE (80 % on average). Comparisons to
surface and remote sensing observations show that the models can simulate
enhancements of a good order of magnitude and ±1-day uncertainty in the
timing.

Two critical parameters, SOA formation from I/S-VOCs and injection heights,
are considered as the two main sources of uncertainties in the calculation of
wildfire impact on surface PM2.5 concentrations and AOD. Sensitivities
to these key parameters are computed using simulations performed with
different configurations of Polyphemus. These configurations are chosen to
maximize the sensitivities.

Compared to satellite observations, the AOD modeled in simulations including
I/S-VOCs is overestimated during the fire events, by about 30 % for
Poly-ref and Poly-3 km and 10 % for
Poly-NoREM-PM2.5, mainly in the two sub-regions MedReg1 and
MedReg2, closest to fire emissions. Since unidentified primary particles
(REM-PM2.5) emitted from biomass burning are not considered in
Poly-NoREM-PM2.5, the simulated AOD values are closer to MODIS
observations. This suggests that REM-PM2.5 could correspond to I/S-VOCs.

Comparisons to AIRBASE measurements show a good simulation of the surface
PM2.5 concentrations during the first fire event (at GR0039A in Greece,
sub-region MedReg2). The reference simulation (Poly-ref) shows
closest comparisons (with a high correlation coefficient, >88 %, and a
low bias), while PM2.5 peaks are slightly underestimated by all the
other simulations. However, the measured AOD tends to be underestimated by
all model simulations at Bucharest (Romania, sub-region MedReg1). During the
second fire event, surface PM2.5 concentrations in simulations Poly-3 km and CHIMERE-ref are in good agreement with AIRBASE
measurements, but are underestimated in other simulations. The modeled AOD is
well simulated compared to AERONET observations at Lecce University and Blida
by CHIMERE-ref and all the Polyphemus simulations except for
Poly-noI/S-VOCs, which shows low AOD values at these stations. This
suggests that taking into account I/S-VOCs improves the simulated AOD values
at these stations. In spite of the uncertainty on fire emissions
(>100 %) and on observations, this analysis shows that the models
succeed to reproduce the PM2.5 concentrations and AOD during such large
wildfire event.

Further analysis of uncertainties is conducted at the regional scale based
solely on the set of sensitivity simulations conducted with the Polyphemus
model. AOD is particularly sensitive to I/S-VOC emissions (up to 40 %
sensitivity), while surface PM2.5 concentrations are particularly
sensitive to the injection heights (up to 50 % sensitivity). These
sensitivities are most of the time higher than inter-model sensitivities,
which are mostly linked to the model vertical discretization close to fire
emissions. The statistical dispersion of the ensemble of simulations based on
different configurations of Polyphemus is used to evaluate the maximum
uncertainty on surface PM2.5 concentrations and AOD associated with
these two parameters. During the summer of 2007, the maximum statistical
dispersion (σ) is as high as 75 % for surface PM2.5 in the
Balkans and Greece and varies between 36 % and 45 % for AOD above
fire regions. The number of daily exceedances of the WHO recommendation of
25 µg m−3 for the PM2.5 24 h mean reaches 30 days for
the fire region and 10–15 days for the fire plume over the simulated period
of 46 days. The maximum statistical dispersion on this indicator is large,
reaching 15 days close to fires and 5–10 in the fire plume.

Although relatively high, this estimate of uncertainty is very conservative
since many other parameters may alter the quality of the simulations of
wildfire impact on atmospheric composition. In addition to uncertainty on
emissions (initial fire characteristics, vegetation type and fraction burned,
emission factors, etc.) and SOA formation, the formation of inorganic
aerosols and the uncertainty on meteorological conditions (pyroconvection but
also general stability in the region) and on deposition are a few examples of
processes that may alter the simulated aerosol budgets. More integrated
surface and in situ observations would be necessary for a precise evaluation
of emissions, the simulated long-range transport from fire emissions, the
aerosol speciation within the plumes and the resulting impact on air quality.

Figure B1Daily mean surface PM2.5 and AOD at 550 nm from the
CHIMERE-ref simulation averaged over the summer of 2007 (the
eight AIRBASE and six AERONET stations, used in this work, are represented
here by blue dots).

Figure C1(a) Relative difference of surface PM2.5
concentrations between simulations CHIMERE-ref and CHIMERE-Nofires during the first fire event. (b) Relative
difference of surface PM2.5 concentrations between simulations CHIMERE-ref and CHIMERE-Nofires during the second fire
event.

MM, ST and KS performed the simulations with help from CL and YK for the preprocessing
and post-processing. MM and ST prepared emissions from fires. MM, KS
and ST prepared the manuscript with contributions from all the co-authors.

CEREA and LMD are members of the Institut Pierre-Simon Laplace (IPSL). This
research was partially funded by a PhD grant from École des Ponts
ParisTech, and by the French ministry of ecology, by CNRS-INSU (GMES-MDD
program, NATORGA project). This study also received financial support from
MISTRALS by ADEME, CEA, INSU, and Meteo-France, as part of the ChArMEx
project. We are grateful to the EEA for maintaining and providing the AirBase
database (more particularly for the GR0039A station). We thank the principal
investigators and their staff for establishing and maintaining the AERONET
sites used in this study: Brent Holben for Blida, Maria Rita Perrone for
Lecce University and Doina Nicolae for Bucharest Inoe. We wish to thank the
National Aeronautics and Space Agency (NASA) for the availability of the
MODIS data. Finally, we thank the anonymous reviewers and editors for their
comments which have helped improve the manuscript.

Hernandez, C., Drobinski, P., and Turquety, S.: How much does weather control fire size and intensity in the Mediterranean
region?, Ann. Geophys., 33, 931–939, https://doi.org/10.5194/angeo-33-931-2015, 2015. a

Nenes, A., Pandis, S., and Pilinis, C.: Continued development and testing of a
new thermodynamic aerosol module for urbban and regional air quality models,
Atmos. Environ., 33, 1553–1560, https://doi.org/10.1016/S1352-2310(98)00352-5, 1999. a